Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple organs and systems. Ocular involvement is estimated to manifest in one-third of individuals with SLE, of which lupus retinopathy and choroidopathy represent the severe subtype accompanied by vision impairment. Advancements in multimodal ophthalmic imaging have allowed ophthalmologists to reveal subclinical microvascular and structural changes in fundus of patients with SLE without ocular manifestations. Both ocular manifestations and subclinical fundus damage have been shown to correlate with SLE disease activity and, in some patients, even precede other systemic injuries as the first presentation of SLE. Moreover, ocular fundus might serve as a window into the state of systemic vasculitis in patients with SLE. Given the similarities of the anatomy, physiological and pathological processes shared among ocular fundus, and other vital organ damage in SLE, such as kidney and brain, it is assumed that ocular fundus involvement has implications in the diagnosis and evaluation of other systemic impairments. Therefore, evaluating the fundus characteristics of patients with SLE not only contributes to the early diagnosis and intervention of potential vision damage, but also holds considerate significance for the evaluation of SLE vasculitis state and prediction of other systemic injuries.

In order to determine the possible aqueous humor (AH) proteins involved in diabetic nephropathy (DN) progression, we performed gel electrophoresis‐liquid chromatography‐tandem mass spectrometry protein profiling of AH samples from 5 patients with proliferative diabetic retinopathy (PDR) combined DN and 5 patients with PDR. Function enrichment analyses were carried out after the identification of differentially expressed proteins (DEPs). Protein‐protein interaction networks were then built and the Search Tool for the Retrieval of Interacting Genes database and CytoNCA plugin in Cytoscape were utilized for module analysis. Ingenuity Pathway Analysis (IPA) was used to analyze disease and biological function, Tox function enrichment and upstream regulatory molecules/networks. Fifty‐four DEPs were finally confirmed, whose enriched functions and pathways covered cell adhesion, extracellular exosome, complement activation, complement and coagulation cascades, etc. Nine hub genes were identified, including NCAM1, PLG, APOH, C3, PSAP, RBP4, CDH2, NUCB1, and GNS. IPA showed that C3 and PLG are involved in renal and urological system abnormalities. Conclusively, DEPs and hub proteins confirmed in this exploratory AH proteomic analysis may help us gain a deeper understanding of the molecular mechanisms involved in DN progression, providing novel candidate biomarkers for the early detection for diagnosis of DN.

Summary: Background: Purtscher-like retinopathy (PLR) is a rare ocular manifestation in systemic lupus erythematosus (SLE) with poor prognosis, but its clear risk factors and treatment consensus are still lacking. Aim: To investigate the clinical features, risk factors and prognosis of PLR in SLE patients. Design and methods: A retrospective analysis was conducted on SLE patients with PLR admitted at Peking Union Medical College Hospital from 2013 to 2022. Clinical data, including demographic characteristics, lupus-related features, laboratory findings and ophthalmologic examinations, were collected and analyzed. The prognosis was evaluated based on best-corrected visual acuity and ophthalmologic outcomes. Results: Seventeen SLE patients (32 eyes) diagnosed with PLR were included, along with a random selection of 100 SLE patients without retinopathy and 100 with retinal microvasculopathy as controls. Patients with PLR had a significantly younger age, a higher proportion of hemolytic anemia, a shorter duration of SLE, a higher SLE disease activity index-2000 (SLEDAI-2K) score, higher erythrocyte sedimentation rate (ESR) values and lower hemoglobin (HGB) values than the group without retinopathy (P < 0.05). They also had a significantly higher SLEDAI-2K score, higher ESR values and higher white blood cell values (P < 0.05) than the Microvasculopathy group. The majority of eyes (22/26, 84.62%) achieved stabilization at the last follow-up, with different therapeutic strategies, while a few (4/26, 15.38%) experienced complications or progression. Conclusion: This is the largest reported case series of PLR in SLE, which was associated with higher disease activity and poor visual prognosis. It was also associated with younger age, shorter SLE duration, concomitant hemolytic anemia, lower HGB and higher ESR value. Early recognition and prompt treatment are crucial for improving visual outcomes.

Background Pathological myopia (PM) has emerged as a leading cause of global visual impairment, early detection and precise grading of PM are crucial for timely intervention. The atrophy, traction and neovascularisation (ATN) system is applied to define PM progression and stages with precision. This study focuses on constructing a comprehensive PM image dataset comprising both fundus and optical coherence tomography (OCT) images and developing a bimodal artificial intelligence (AI) classification model for ATN grading in PM. Methods This single-centre retrospective cross-sectional study collected 2760 colour fundus photographs and matching OCT images of PM from January 2019 to November 2022 at Peking Union Medical College Hospital. Ophthalmology specialists labelled and inspected all paired images using the ATN grading system. The AI model used a ResNet-50 backbone and a multimodal multi-instance learning module to enhance interaction across instances from both modalities. Results Performance comparisons among single-modality fundus, OCT and bimodal AI models were conducted for ATN grading in PM. The bimodality model, dual-deep learning (DL), demonstrated superior accuracy in both detailed multiclassification and biclassification of PM, which aligns well with our observation from instance attention-weight activation maps. The area under the curve for severe PM using dual-DL was 0.9635 (95% CI 0.9380 to 0.9890), compared with 0.9359 (95% CI 0.9027 to 0.9691) for the solely OCT model and 0.9268 (95% CI 0.8915 to 0.9621) for the fundus model. Conclusions Our novel bimodal AI multiclassification model for PM ATN staging proves accurate and beneficial for public health screening and prompt referral of PM patients.

Smartphone-based portable slit lamps are rapidly evolving from simple add-ons into practical, low-cost front-line tools for anterior segment care beyond traditional clinical settings. By integrating high-performance smartphone cameras with compact optical attachments (e.g., slit-light converters, macro lenses, blue filters) and dedicated applications, these devices deliver slit-lamp-style imaging and video capture to environments where conventional biomicroscopes are inaccessible. Accumulating clinical evidence—most notably for the Smart Eye Camera (SEC) and iSpector MINI HE 010-21—confirms that smartphone slit lamps reliably support assessments across major anterior segment disorders. SEC-based recordings enable evaluation of tear film–related signs in dry eye disease; smartphone-acquired slit-lamp images show strong agreement with standard slit lamps for corneal ulcers, scars, and other ocular surface pathologies; slit-beam acquisition facilitates preliminary screening of shallow anterior chambers and narrow angles relevant to primary angle-closure glaucoma; and cataract screening and grading via smartphone systems align closely with conventional slit-lamp evaluations. Notably, recent advancements have transcended mere image capture to embrace artificial intelligence (AI)-enabled analysis, positioning smartphone slit lamps as scalable screening and triage solutions. Across the studies reviewed, AI models trained on smartphone slit-lamp images or videos demonstrate robust feasibility for automated dry eye diagnosis, corneal opacity detection, keratitis screening, cataract grading, pterygium detection/grading, and narrow-angle identification, typically through pipelines integrating image quality control, region-of-interest localization/segmentation, and disease-specific prediction. Despite these advances, however, key barriers remain, including incomplete replication of full slit-lamp functionality, lack of standardized acquisition protocols, and limited multicenter external validation for most AI systems. Future progress should prioritize hardware stabilization, optical design improvements, disease-specific standardized imaging workflows, and large-scale prospective validation to unlock the full potential of AI-assisted smartphone slit lamps for community screening, teleophthalmology, and care in underserved regions.